Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

Image and video datasets and models for mxnet deep learning

Project Description

# MXbox: Simple, efficient and flexible vision toolbox for mxnet framework.

MXbox is a toolbox aiming to provide a general and simple interface for vision tasks. This project is greatly inspired by [PyTorch]( and [torchvision]( Detailed copyright files are on the way. Improvements and suggestions are welcome.

## Installation
pip install mxbox

## Features
1. Define **preprocess** as a flow

transform = transforms.Compose([
transforms.RandomHorizontalFlip(),, = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),

PS: By default, mxbox uses `PIL` to read and transform images. But it also supports other backends like `accimage` and `skimage`.

More examples can be found in XXX.

2) Build **DataLoader** in several lines

feedin_shapes = {
'batch_size': 8,
'data': ['data', shape=(8, 3, 32, 32), layout='NCHW')],
'label': ['softmax_label', shape=(8, 1), layout='N')]

dst = Dataset(root='../../data', transform=img_transform, label_transform=label_transform)
loader = DataLoader(dst, feedin_shapes, threads=8, shuffle=True)

Also, common datasets such as `cifar10`, `cifar100`, `SVHN`, `MNIST` are out-of-the-box. You can simply load them from `mxbox.datasets`.

3) Load popular model with pretrained weights

vgg = mxbox.models.vgg(num_classes=10, pretrained=True)
resnet = mxbox.models.resnet152(num_classes=10, pretrained=True)

## Documentation

Under construction, coming soon.

## TODO list

1) Efficient multi-thread reading (Prefetch wanted

2) Common Models preparation.

3) More friendly error logging.

Release History

This version
History Node


History Node


History Node


History Node


Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Hash SHA256 Hash Help Version File Type Upload Date
(33.2 kB) Copy SHA256 Hash SHA256
py2.py3 Wheel Aug 8, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting